Instructions to use Monu0810/flan-t5-summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Monu0810/flan-t5-summarizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Monu0810/flan-t5-summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("Monu0810/flan-t5-summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
flan-t5-summarizer
This model is a fine-tuned version of google/flan-t5-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4004
- Rouge1: 0.4217
- Rouge2: 0.1993
- Rougel: 0.3033
- Rougelsum: 0.3034
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 13.0810 | 1.0 | 188 | 1.4028 | 0.4218 | 0.1985 | 0.3041 | 0.3046 |
| 13.0241 | 2.0 | 376 | 1.4004 | 0.4217 | 0.1993 | 0.3033 | 0.3034 |
Framework versions
- Transformers 5.18.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.2
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Model tree for Monu0810/flan-t5-summarizer
Base model
google/flan-t5-large